def SHA256(password):
password = password.encode('utf-8')
password = pr.Pre_processing(password)
password = pr.cut(password)
length = len(password)
data = str(length)
for _ in range(length):
password[_] = pr.cut(password[_], 32)
for i in range(len(password[_])):
password[_][i] = int(password[_][i], base=2)
data += ' ' + str(password[_][i])
#os.system('C:\\Users\\admin\\Documents\\XXX\\python\\Project X\\SHA256\\main_loop.exe '
# + data)
system('"C:\\Users\\admin\\Documents\\XXX\\python\\Project_X\\SHA256\\main_loop.exe ' + data + '"')
with open('C:\\Users\\admin\\Documents\\XXX\\python\\Project_X\\SHA256\\bits\\result.txt', 'r') as dg:
digest = dg.read()
digest = digest.split()
for _ in range(8):
if len(digest[_]) < 8:
while len(digest[_]) < 8:
digest[_] = '0' + digest[_]
return ''.join(digest)
def __init__(self, filePath, zoomFactor, translationFactor):
self.X_train = []
self.X_predict = []
self.filePath = filePath
self.zoom_factor = zoomFactor
self.translation_factor = translationFactor
# 获取训练集数据
allPreData = pd.read_csv('./data/originTrainData.csv').values.flatten().tolist()
allPreDataArr = cut(allPreData, frequency)
for index in allPreDataArr:
self.X_train.extend(getFeature(np.array([index])))
self.X_train = self.trainNormalizedV2(self.X_train)
# 获取测试集数据
txtFile = np.transpose(np.loadtxt(self.filePath, dtype=np.float128))
self.X_predict.extend(getFeature(np.array([txtFile])))
# X_predict = origin2train(X_predict, allPreData)
self.X_predict = self.testNormalizedV2(self.X_predict)
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
# data loading
import preprocessing as tool
data_path = 'C:/Users/Jaeyun/PycharmProjects/Unstructured_tensor/article_class_text.txt'
corpus, importants = tool.loading_rdata(data_path, eng=True, num=True, punc=False)
contents, importants = tool.cut(corpus, importants, cut=2, threshold=10)
# tranform document to vector
# max_document_length = tool.check_maxlength(corpus)
max_document_length = 100
x, vocabulary, vocab_size, vocab_processor = tool.make_input(contents, max_document_length)
print('사전단어수 : %s' % (vocab_size))
y = tool.make_output(importants)
# divide dataset into train/test set
x_train, x_test, y_train, y_test, train_idx = tool.divide(x, y, train_prop=0.8)
# Model Hyperparameters
flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of embedded vector (default: 128)")
flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
def SHA256(password):
password = password.encode('utf-8')
password = pr.Pre_processing(password)
password = pr.cut(password)
rows = len(password)
cols = 64
s = []
w = [[0 for col in range(cols)] for row in range(rows)]
data = str(rows)
for _ in range(rows):
password[_] = pr.cut(password[_], 32)
for i in range(len(password[_])):
password[_][i] = int(password[_][i], base=2)
data += ' ' + str(password[_][i])
s.append(password[_][i])
for i in range(rows):
for j in range(16):
w[i][j] = np.uint32(s[i * 16 + j])
H = np.array([
0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, 0x510e527f, 0x9b05688c,
0x1f83d9ab, 0x5be0cd19
],
dtype=np.uint32)
k = np.array([
0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1,
0x923f82a4, 0xab1c5ed5, 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3,
0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, 0xe49b69c1, 0xefbe4786,
0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,
0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147,
0x06ca6351, 0x14292967, 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13,
0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, 0xa2bfe8a1, 0xa81a664b,
0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,
0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a,
0x5b9cca4f, 0x682e6ff3, 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208,
0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2
],
dtype=np.uint32)
for j in range(rows):
for i in range(16, 64):
s0 = np.uint32(
rotr(w[j][i - 15], 7) ^ rotr(w[j][i - 15], 18)
^ (w[j][i - 15] >> 3)) % 4294967296
s1 = np.uint32(
rotr(w[j][i - 2], 17) ^ rotr(w[j][i - 2], 19)
^ (w[j][i - 2] >> 10)) % 4294967296
w[j][i] = (w[j][i - 16] + s0 + w[j][i - 7] + s1) % 4294967296
a = np.uint32(H[0])
b = np.uint32(H[1])
c = np.uint32(H[2])
d = np.uint32(H[3])
e = np.uint32(H[4])
f = np.uint32(H[5])
g = np.uint32(H[6])
h = np.uint32(H[7])
for i in range(64):
E0 = (rotr(a, 2) ^ rotr(a, 13) ^ rotr(a, 22)) % 4294967296
Ma = ((a & b) ^ (a & c) ^ (b & c)) % 4294967296
t2 = (E0 + Ma) % 4294967296
E1 = (rotr(e, 6) ^ rotr(e, 11) ^ rotr(e, 25)) % 4294967296
Ch = ((e & f) ^ ((~e) & g)) % 4294967296
t1 = (h + E1 + Ch + k[i] + w[j][i]) % 4294967296
h = g
g = f
f = e
e = (d + t1) % 4294967296
d = c
c = b
b = a
a = (t1 + t2) % 4294967296
H[0] = (H[0] + a) % 4294967296
H[1] = (H[1] + b) % 4294967296
H[2] = (H[2] + c) % 4294967296
H[3] = (H[3] + d) % 4294967296
H[4] = (H[4] + e) % 4294967296
H[5] = (H[5] + f) % 4294967296
H[6] = (H[6] + g) % 4294967296
H[7] = (H[7] + h) % 4294967296
digest = []
for _ in range(8):
digest.append(str(hex(H[_]))[2:])
for _ in range(8):
if len(digest[_]) < 8:
while len(digest[_]) < 8:
digest[_] = '0' + digest[_]
return ''.join(digest)
for _ in range(8):
if len(digest[_]) < 8:
while len(digest[_]) < 8:
digest[_] = '0' + digest[_]
return ''.join(digest)
# file = open('input_data/text.txt', 'rb')
# text = file.read()
# file.close()
# print(type(text))
if __name__ == '__main__':
text = input('input: ').encode('utf-8')
text = pr.Pre_processing(text)
text = pr.cut(text)
length = len(text)
data = str(length)
for _ in range(length):
text[_] = pr.cut(text[_], 32)
for i in range(len(text[_])):
text[_][i] = int(text[_][i], base=2)
data += ' ' + str(text[_][i])
system('main_loop.exe ' + data)
with open('bits/result.txt', 'r') as dg:
digest = dg.read()
digest = digest.split()
for _ in range(8):